An agricultural tele-monitoring method in detecting nutrient deficiencies of oil palm leaf

• Main Authors: Hussain, A. (Author), Muhammad Asraf, H. (Author), Nur Dalila, K.A (Author), Tahir, N.M (Author)
• Format: Article
• Language: English
• Published: Science Publishing Corporation Inc 2018
• Subjects: Deficiencies detection; Leaf disease; Machine learning classifier; Oil palm; SVM (Support Vector Machine)
• Online Access: View Fulltext in Publisher; View in Scopus

 Nutrient management in oil palm plantation is considered as one of the prominent issues especially for smallholder farmer. The nutrient contained in the tress has always been neglected and untreated and these may cause the trees to suffer from nutrient deficiencies. Therefore, in leveraging the oil yield at the maximum, a telemonitoring system is developed to assess and monitor the lack of nutrients for respective trees. This is done using image processing technique and artificial intelligence in detecting the nutritional deficiencies by analyzing the leaf. The categorization focused by classifying into four major types either as magnesium deficiencies, potassium deficiencies, nitrogen deficiencies or healthy that is based on the oil palm's leaf surface. This is achieved by extracting the features namely number of red pixels, entropy and correlations. Further, two classifiers specifically support vector machine and artificial neural network is used for classification purpose along with performance measure using accuracy(ACC), Mean Square Error (MSE), Mean Absolute Error (MAE), Sensitivity (SN), Specificity (SP), Positive Predictive Value (PPV), Negative Predictive Value (NPV) based on ten-fold cross-validation. Results attained showed that the best classifier is SVM using RBF kernel (SVM-RBF) that is capable to accurately recognize the nutrient deficiencies with 100% accuracy. © 2018 Authors.